Scenario aware perception system for an automated vehicle

ABSTRACT

A scenario aware perception system (10) suitable for use on an automated vehicle includes a traffic-scenario detector (14), an object-detection device (24), and a controller (32). The traffic-scenario detector (14) is used to detect a present-scenario (16) experienced by a host-vehicle (12). The object-detection device (24) is used to detect an object (26) proximate to the host-vehicle (12). The controller (32) is in communication with the traffic-scenario detector (14) and the object-detection device (24). The controller (32) configured to determine a preferred-algorithm (36) used to identify the object (26). The preferred-algorithm (36) is determined based on the present-scenario (16).

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to PCTApplication No. PCT/US2017/016154, filed Feb. 2, 2017, which claimspriority to U.S. patent application Ser. No. 15/076,951, filed Mar. 22,2016, issued as U.S. Pat. No. 9,898,008 on Feb. 20, 2018, the entirecontents of each of which are incorporated herein by reference.

TECHNICAL FIELD OF INVENTION

This disclosure generally relates to a scenario aware perception system,and more particularly relates to a system that determines or selects apreferred-algorithm used to identify the object based on thepresent-scenario

BACKGROUND OF INVENTION

It is known to equip an automated vehicle with various sensors such as acamera, a radar-unit, and/or a lidar-unit to detect various objects suchas other vehicles and roadway features for controlling the steering andbraking of the host-vehicle. However, the other-vehicles may presentthemselves to the sensors with an unknown orientation, which makessignal processing of signal from the sensors more difficult.

SUMMARY OF THE INVENTION

The problems described above may be overcome if the orientation or view(e.g. back-view vs. side-view vs. front-view) of another vehicle wasknown or expected. If the expected perspective of the other vehicleswere known, the processing of signals necessary to reliably identify anobject as another-vehicle would be simplified. Described herein is acontext or scenario aware perception system for operating an automatedvehicle (e.g. a host-vehicle) that uses information from atraffic-scenario detector that may include one or more sensors (e.g.camera, radar, and/or lidar) and/or a digital-map to identify orclassify the present traffic-situation or present-scenario of thehost-vehicle as one of a variety of previously identifiedpossible-scenarios. For example, the system may classify thepresent-scenario by selecting from a predetermined list or plurality ofpossible-scenarios that best matches the present-scenario experienced bythe host-vehicle.

Then the system selects a preferred-algorithm for processing the signalsfrom an object-detection device that may use some of the same sensors asthe traffic-scenario detector, where the preferred-algorithm selectedwas previously optimized for the present traffic situation orpresent-scenario. In other words, the system selects thepreferred-algorithm from a list of previously definedoptimized-algorithms, and the preferred-algorithm that is selected is analgorithm that was optimized for the present-scenario.

The present-scenario may be determined using one or more sensors and/ormap-data. By way of example and not limitation, the present-scenario maybe determined based on a first-signal from a first-sensor such as aradar-unit, but the preferred-algorithm is applied to a second-signalfrom a second-sensor such as a camera. By way of further example thepreferred-algorithm that is selected may have been optimized for theexpected-motion of a possible-target (e.g. other-vehicle) that might bedetected using the sensor, or the expected-motion of a detected-targetthat has been detected using the sensor or other-sensors, OR is beingcurrently tracked using the sensor.

The expected-motion may be determined based on road-geometry and/oranticipated-motion of the sensor arising from an upcomingvehicle-maneuver. The road-geometry may be determined using the sensor,and/or other-sensors and/or map-data. Examples of an upcomingvehicle-maneuver include: a lane-change, a turn across on-comingtraffic, and following an other-vehicle on curved road. If a radar-unitis being used as a sensor, the system may select a mode of radar signalprocessing that is optimized for features of the roadway systemproximate to the host-vehicle based on map-data.

By way of further example, the system may select a signal-processingalgorithm to process a signal from an object-detection device, where thealgorithm is optimized for an expected direction of travel of another-vehicle which is determined based on map data from a digital-mapat a map-location determined by a location-indicator. The system mayselect an algorithm for image processing based on the expectedperspective (e.g. side-view vs. rear-view of other-vehicle) of theother-vehicle to classify the other-vehicle. a target based on map-data(what direction is the other host-vehicle likely traveling). The systemmay use Lidar to detect when sides of an object are exposed so thecamera image processing can ignore part of the image information andmore reliably determine that the object is another host-vehicle. Abenefit of selecting an optimized algorithm is that safety is improvedbecause less time spent looking for unlikely image matches.

By way of further example, a first scenario is when an other-vehicletraveling in a lane adjacent to that of the host-vehicle, and theother-vehicle changes lanes and moves to a position in front of thehost-vehicle, i.e. the other-vehicle cuts in. Because an optimizedalgorithm was selected for tracking the other-vehicle, the system isable to identify and track the cutting-in other-vehicle faster and morereliably. The preferred-algorithm is selected by using the mapinformation, so the relative location of neighboring lanes is known. Thesystem then tracks the closest of other-vehicles leading thehost-vehicle in the neighboring lanes and computes their lateralvelocity. In response to detecting that the other-vehicle is cutting in,the host-vehicle starts to perform distance keeping relative to thecutting-in other-vehicle.

A second scenario is ramp merging when the host-vehicle is driving in atravel-lane of a roadway and other-vehicle is on a ramp merging into thetravel-lane. A preferred-algorithm is selected to identify and track themerging other-vehicle. Map-data is used to determine the presence of theramp. The system then tracks the other-vehicle on the ramp which is theclosest one to a merging-point. Based on the velocity and accelerationof that other-vehicle, the system computes the time to arrival of themerging-point for the host-vehicle and the merging other-vehicle. Thehost-vehicle may elect to slow-down or speed-up depending on therelative location of the merging-point and/or a time to arrival.

In accordance with one embodiment, a scenario aware perception systemsuitable for use on an automated vehicle is provided. The systemincludes a traffic-scenario detector, an object-detection device, and acontroller. The traffic-scenario detector is used to detect apresent-scenario experienced by a host-vehicle. The object-detectiondevice is used to detect an object proximate to the host-vehicle. Thecontroller is in communication with the traffic-scenario detector andthe object-detection device. The controller configured to determine apreferred-algorithm used to identify the object. The preferred-algorithmis determined based on the present-scenario.

Further features and advantages will appear more clearly on a reading ofthe following detailed description of the preferred embodiment, which isgiven by way of non-limiting example only and with reference to theaccompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

The present invention will now be described, by way of example withreference to the accompanying drawings, in which:

FIG. 1 is a diagram of a scenario aware perception system in accordancewith one embodiment;

FIG. 2 is traffic scenario encountered by the system of FIG. 1 inaccordance with one embodiment; and

FIG. 3 is traffic scenario encountered by the system of FIG. 1 inaccordance with one embodiment.

DETAILED DESCRIPTION

FIG. 1 illustrates a non-limiting example of a scenario aware perceptionsystem 10, hereafter referred to as the system 10, which is generallysuitable for use on an automated vehicle, hereafter referred to as thehost-vehicle 12. The system 10 includes a traffic-scenario detector 14used to determine or detect a present-scenario 16, i.e. atraffic-situation, presently being experienced by the host-vehicle 12.As used herein, the present-scenario 16 may be characterized by theconfiguration of a roadway 18 proximate to the host-vehicle 12. By wayof example and not limitation, the present-scenario 16 may becharacterized as a multilane expressway (FIG. 2), a two-lane road withan entrance ramp (FIG. 3), a four-way-stop intersection withintersecting two-lane roads oriented at an angle (not limited to aright-angle), a residential roadway lined with driveways to individualresidences, and a round-about type intersection with multiple roadwaysjoined to the round-about at a variety of angles.

The traffic-scenario detector 14 may be or may include, but is notlimited to, a camera, a radar-unit, a lidar-unit, or any combinationthereof that could be useful to characterize or determine thepresent-scenario 16 of the host-vehicle 12, where the present-scenario16 is determined based on a signal from the traffic-scenario detector14. In addition, or as an alternative, the traffic-scenario detector 14may include a location-indicator 20 that, for example, determines theglobal-coordinates of the host-vehicle 12 so the system 10 can determinethe present-scenario 16 by consulting a digital-map 22 that indicates,for example, the number of lanes of the roadway 18, presence of anentrance or exit ramp, intersection controls (e.g. traffic-signal orstop-sign), and the like. That is, the traffic-scenario detector 14 mayinclude a location-indicator 20, and the present-scenario 16 may bedetermined based on a map-location 40 of the host-vehicle 12 on thedigital-map 22 as indicated by the location-indicator 20.

The system 10 also includes an object-detection device 24 used to detectan object 26 proximate to the host-vehicle 12. The object-detectiondevice 24 may be or may include, but is not limited to, a camera,radar-unit, lidar-unit, or any combination thereof that could be usefulto identify or classify the object 26. The object 26 may be, but is notlimited to, the roadway 18, features that define boundaries of theroadway 18, an other-vehicle 28, a fixed-object 30 such as atraffic-barrier, building, sign, tree, or any other instance of theobject 26 that could be the fixed-object 30.

The system 10 also includes a controller 32 in communication with thetraffic-scenario detector 14 and the object-detection device 24. Thecontroller 32 may include a processor 34 such as a microprocessor orother control circuitry such as analog and/or digital control circuitryincluding an application specific integrated circuit (ASIC) forprocessing data, as should be evident to those in the art. Thecontroller 32 may include memory 42, including non-volatile memory, suchas electrically erasable programmable read-only memory (EEPROM) forstoring one or more routines, thresholds, and captured data. The one ormore instances of possible-scenarios 44 and/or optimized-algorithms 46that may be used the processor to perform steps to determine apreferred-algorithm 36 used to identify or determine anobject-identification 38 of the object 26, where the preferred-algorithm36 is determined based on the present-scenario 16, as will be describedin more detail below. Accordingly, the controller 32 may be configuredto select the present-scenario 16 from a plurality of possible-scenarios44 stored in the memory 42.

FIG. 2 illustrates a non-limiting example of the present-scenario 16that may be characterized as a multilane-expressway where theother-vehicle is traveling in an adjacent-lane 50 that is adjacent to ornext to a travel-lane 52 of the host-vehicle 12. The controller 32 maybe tracking the other-vehicle 28 using the object-detection device 24.Information from the traffic-scenario detector 14 such as data from thedigital-map 22 and/or images from the camera indicate that thepresent-scenario 16 is a multilane-expressway. The preferred-algorithm36 selected from the optimized-algorithms 46 is optimized for trackingthe other-vehicle 28 along an expected-direction 54 that is parallel toand offset from a travel-direction 56 of the host-vehicle 12. As such,the controller 32 is configured to further determine or select thepreferred-algorithm 36 based on the expected-direction 54 of motion ofthe object 26 relative to the host-vehicle 12 indicated by thepresent-scenario 16.

The preferred-algorithm 36 may be selected for processing signals fromthe camera or radar-unit because the perspective the other-vehicle 28 isquartering away from the host-vehicle 12. That is, an image of orradar-reflection from the other-vehicle will likely include data-pointsthat correspond to the tail-end and left-side of the other-vehicle. Byway of further example, the processor 34 may attempt to match thepresent-image from the camera to one of a plurality of previously storedimages, or match the radar-reflection to a predeterminedreflection-pattern.

The preferred-algorithm 36 may also be selected or optimized to detectlateral motion of the other-vehicle 28 which would occur if theother-vehicle 28 executed a lane-change 58 i.e. moves to a position infront of the host-vehicle 12 or ‘cuts-in’ to the travel-lane 52. Becausean optimized algorithm was selected to monitor for lateral motion, thesystem is able to identify and track the cutting-in by other-vehicle 28faster and more reliably. The preferred-algorithm may also selected byusing the digital-map 22 since the relative location of theadjacent-lane 50 is known. The system then tracks the closest ofother-vehicles leading the host-vehicle 12 in neighboring lanes andcomputes their lateral velocity. In response to detecting that theother-vehicle 28 is cutting in, the host-vehicle 12 may begin to performdistance keeping relative to the other-vehicle after the cutting-inevent.

FIG. 3 illustrates a non-limiting example of the present-scenario 16that may be characterized as a two-lane road 60 with an entrance-ramp 62that will merge the other-vehicle into the travel-lane 52. The presenceof the entrance-ramp 62 may be determined from the digital-map 22 basedon information from the location-indicator 20. The preferred-algorithm36 may be selected from the optimized-algorithms 46 as one that isoptimized to track a vehicle traveling beside but not necessarilyparallel to the host-vehicle 12, and one that can readily determine therelative-speed of the other-vehicle 28 with respect to the host-vehicle12. Based on the relative-speed and acceleration of the other-vehicle 28with respect to the host-vehicle 12, the system 10 computes atime-to-arrival to a merging-point 64 for the host-vehicle 12 and theother-vehicle 28, and determines if a conflict is likely. Thehost-vehicle 12 may elect to slow-down or speed-up depending on thelocation of the merging-point 64 and/or relative times-to-arrival.

Accordingly, a scenario aware perception system (the system 10), acontroller 32 for the system 10, and a method of operating the system 10is provided. The preferred-algorithm used to process signals from theobject-detection device 24 is selected based on the present-scenario 16being experienced by the host-vehicle 12. By selecting an algorithm thathas been optimized for the present-scenario, the reliability of trackingthe object 26, e.g. the other-vehicle 28, is improved.

While this invention has been described in terms of the preferredembodiments thereof, it is not intended to be so limited, but ratheronly to the extent set forth in the claims that follow.

1.-7. (canceled)
 8. A method for controlling an autonomous vehicle,comprising: determining a present traffic scenario of a plurality ofpossible traffic scenarios of the autonomous vehicle, wherein eachpossible traffic scenario characterizes a configuration of a roadway;selecting a respective object identification algorithm associated withthe present traffic scenario; and identifying one or more target objectsproximate to the autonomous vehicle using the selected objectidentification algorithm.
 9. The method of claim 8, wherein the selectedobject identification algorithm identifies target objects that move inan expected direction of travel on the roadway characterized by thepresent traffic scenario.
 10. The method of claim 8, wherein identifyingone or more target objects proximate to the autonomous vehicle using theselected object identification algorithm comprises: receiving a radarreflection pattern of the one or more target objects; comparing theradar reflection pattern of the one or more target objects to aplurality of stored reflection patterns associated with the selectedobject identification algorithm; and identifying the one or more targetobjects based on the comparison.
 11. The method of claim 8, whereindetermining the present traffic scenario of the autonomous vehiclecomprises: receiving coordinates defining a location of the autonomousvehicle; determining a configuration of a roadway at the location of theautonomous vehicle using map data; and based on the configuration of theroadway at the location, determining the present traffic scenario. 12.The method of claim 8, wherein determining the present traffic scenarioof the autonomous vehicle comprises: using sensor data to determine theconfiguration of a roadway at the location of autonomous vehicle; andbased on the configuration of the roadway, determining the presenttraffic scenario.
 13. The method of claim 8, wherein the present trafficscenario is a four-way stop intersection, a residential roadway, or aroundabout.
 14. The method of claim 8, wherein the traffic scenario is amultilane expressway, and wherein the selected object identificationalgorithm tracks target vehicles that are parallel to and offset from atravel direction of the autonomous vehicle.
 15. The method of claim 8,wherein the traffic scenario is a two-lane road with an entrance ramp,and wherein the selected object identification algorithm tracks targetvehicles that are beside but not parallel to a travel direction of theautonomous vehicle.
 16. An autonomous vehicle comprising: a trafficscenario detector configured to determine a present traffic scenario ofa plurality of possible traffic scenarios of the autonomous vehicle,wherein each possible traffic scenario characterizes a configuration ofa roadway; a controller configured to select a respective objectidentification algorithm associated with the present traffic scenario;and an object detection device configured to identify one or more targetobjects proximate to the autonomous vehicle using the selected objectidentification algorithm.
 17. The autonomous vehicle of claim 16,wherein the selected object identification algorithm is used to identifytarget objects that move in an expected direction of travel on theroadway characterized by the present traffic scenario.
 18. Theautonomous vehicle of claim 16, wherein identifying, by the objectdetection device, one or more target objects proximate to the autonomousvehicle using the selected object identification algorithm comprises:receiving a radar reflection pattern of the one or more target objects;comparing the radar reflection pattern of the one or more target objectsto a plurality of stored reflection patterns associated with theselected object identification algorithm; and identifying the one ormore target objects based on the comparison.
 19. The autonomous vehicleof claim 16, wherein determining the present traffic scenario of theautonomous vehicle by the traffic scenario detector comprises: receivingcoordinates defining a location of the autonomous vehicle; determining aconfiguration of a roadway at the location of the autonomous vehicleusing map data; and based on the configuration of the roadway at thelocation, determining the present traffic scenario.
 20. The autonomousvehicle of claim 16, wherein determining the present traffic scenario ofthe autonomous vehicle by the traffic scenario detector comprises: usingsensor data to determine the configuration of a roadway at the locationof autonomous vehicle; and based on the configuration of the roadway,determining the present traffic scenario.
 21. The autonomous vehicle ofclaim 16, wherein the present traffic scenario is a four-way stopintersection, a residential roadway, or a roundabout.
 22. The autonomousvehicle of claim 16, wherein the traffic scenario is a multilaneexpressway, and wherein the selected object identification algorithm isused to track target vehicles that are parallel to and offset from atravel direction of the autonomous vehicle.
 23. The autonomous vehicleof claim 16, wherein the traffic scenario is a two-lane road with anentrance ramp, and wherein the selected object identification algorithmis used to track target vehicles that are beside but not parallel to atravel direction of the autonomous vehicle.
 24. One or morenon-transitory computer storage media storing instructions that areoperable, when executed by one or more computers, to cause the one ormore computers to perform operations comprising: determining a presenttraffic scenario of a plurality of possible traffic scenarios of theautonomous vehicle, wherein each possible traffic scenario characterizesa configuration of a roadway; selecting a respective objectidentification algorithm associated with the present traffic scenario;and identifying one or more target objects proximate to the autonomousvehicle using the selected object identification algorithm.